---
id: highspy
version: "1.15.1"
license: MIT
license_treatment: permissive
maintenance: active
---

# highspy — A thin set of pybind11 wrappers to HiGHS

License: permissive · Maintenance: active · Downloads: 3.6M/mo

## What it is and what it does

Highspy provides a thin Python interface to HiGHS, a C++ optimization solver that handles linear, quadratic, and mixed-integer programming problems. It wraps the core solver functionality via pybind11, allowing Python developers to formulate and solve optimization models without writing C++ code. The package depends on numpy for numerical operations and typing_extensions for type hints.

The solver implements primal and dual revised simplex algorithms for LP, an interior-point method for LP, an active-set method for QP, and a branch-and-bound approach for MIP. It is designed for large-scale sparse problems and supports both serial and parallel execution. No third-party solver dependencies are required—HiGHS is self-contained.

Use it for:

- Solve linear programming problems such as resource allocation, production planning, or network flow optimization
- Handle mixed-integer programming for discrete optimization tasks like scheduling or facility location
- Solve convex quadratic programming problems in portfolio optimization or machine learning applications
- Integrate optimization into Python data science workflows alongside numpy and pandas
- Prototype optimization models quickly without switching to specialized modeling languages

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Highspy is a Python wrapper around HiGHS, a high-performance solver for linear programming (LP), convex quadratic programming (QP), and mixed-integer programming (MIP) problems.

Yes. Highspy is actively maintained, widely used (top 5000 PyPI packages), has no known vulnerabilities, and offers a permissive MIT license. Medium install friction is typical for compiled solvers. Install it if you need to solve LP, QP, or MIP problems in Python and want a self-contained, dependency-light solver.

## Install

pip install highspy
uv add highspy
poetry add highspy

## Installing highspy

Before you install: Medium install friction due to compiled wheels for multiple platforms and Python versions (3.9–3.14). Active maintenance with recent releases; last commit 2026-08-14. Depends on numpy and typing_extensions, both widely available.

License in practice: MIT license is permissive; you may use, modify, and distribute highspy and derivative works freely with minimal restrictions.

Quickstart:

pip install highspy
import highspy
h = highspy.Highs()
# Define and solve an optimization model

Verify before relying:

- Whether the solver supports warm-starting from previous solutions or basis information
- Performance characteristics and scalability limits for large-scale problems
- Availability of detailed API documentation beyond the GitHub repository

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 3.6M/month (top 5,000 on PyPI)
- Known vulnerabilities: none known

## Tags

linear programming solver, mixed integer programming, quadratic programming, optimization solver python, highs solver wrapper, convex optimization, mathematical optimization, optimization, linear-programming, solver

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